Makuhari, Chiba, Japan
September 26-30. 2010

On the Exploitation of Hidden Markov Models and Linear Dynamic Models in a Hybrid Decoder Architecture for Continuous Speech Recognition

Volker Leutnant, Reinhold Haeb-Umbach

Universität Paderborn, Germany

Linear dynamic models (LDMs) have been shown to be a viable alternative to hidden Markov
models (HMMs) on small-vocabulary recognition tasks, such as phone classification. In this
paper we investigate various statistical model combination approaches for a hybrid HMM-LDM
recognizer, resulting in a phone classification performance that outperforms the best
individual classifier. Further, we report on continuous speech recognition experiments on
the AURORA4 corpus, where the model combination is carried out on wordgraph rescoring.
While the hybrid system improves the HMM system in the case of monophone HMMs, the
performance of the triphone HMM model could not be improved by monophone LDMs, asking for
the need to introduce context-dependency also in the LDM model inventory.